Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.
Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.
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Parallel-META 2.0：通过功能注释、高性能计算和高级可视化增强宏基因组数据分析
DOI：
10.1371/journal.pone.0089323
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Ning K
中科院分区：
文献类型：
--
作者：
Su X;Pan W;Song B;Xu J;Ning K
元基因组学方法直接对微生物群落的基因组信息进行测序和分析。元基因组分析的主要计算任务包括对微生物群落(也称为元基因组样本)中的所有基因组进行分类和功能结构分析。随着下一代测序技术的发展，元基因组样本的数量和每个样本的数据量都在迅速增加。目前的元基因组分析是数据和计算密集型的，特别是当一个元基因组样本中有许多物种，并且每个物种都有大量的序列时。因此，元基因组分析需要广泛的计算能力。不断增加的分析要求进一步增加了计算分析的挑战。在这项工作中，我们提出了一个并行Meta 2.0，一个元基因组分析软件包，以满足对微生物群落分类和功能结构的高效和快速分析的需求。PARALLEL-META 2.0是PARALLEL-META 1.0的扩展和改进版本，它增强了使用多个数据库进行分类分析的能力，通过优化并行计算提高了计算效率，并支持在多个视图中交互显示结果。此外，它还可以对元基因组样本进行功能分析，包括短读写组装、基因预测和功能注释。因此，它可以以高通量和大规模的方式提供准确的元基因组样本的分类和功能分析。
The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.
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影响因子：
14.9
作者：
Pruesse E;Quast C;Knittel K;Fuchs BM;Ludwig W;Peplies J;Glöckner FO
通讯作者：
Glöckner FO
影响因子：
3.7
作者：
Song B;Su X;Xu J;Ning K
通讯作者：
Ning K
影响因子：
64.8
作者：
Arumugam, Manimozhiyan;Raes, Jeroen;Pelletier, Eric;Le Paslier, Denis;Yamada, Takuji;Mende, Daniel R.;Fernandes, Gabriel R.;Tap, Julien;Bruls, Thomas;Batto, Jean-Michel;Bertalan, Marcelo;Borruel, Natalia;Casellas, Francesc;Fernandez, Leyden;Gautier, Laurent;Hansen, Torben;Hattori, Masahira;Hayashi, Tetsuya;Kleerebezem, Michiel;Kurokawa, Ken;Leclerc, Marion;Levenez, Florence;Manichanh, Chaysavanh;Nielsen, H. Bjorn;Nielsen, Trine;Pons, Nicolas;Poulain, Julie;Qin, Junjie;Sicheritz-Ponten, Thomas;Tims, Sebastian;Torrents, David;Ugarte, Edgardo;Zoetendal, Erwin G.;Wang, Jun;Guarner, Francisco;Pedersen, Oluf;de Vos, Willem M.;Brunak, Soren;Dore, Joel;Weissenbach, Jean;Ehrlich, S. Dusko;Bork, Peer
通讯作者：
Bork, Peer
DOI：
10.1187/cbe.07-09-0075
发表时间：
2007-01-01
期刊：
CBE life sciences education
影响因子：
--
作者：
Jurkowski, Anne;Reid, Ann H;Labov, Jay B
通讯作者：
Labov, Jay B
影响因子：
4.4
作者：
Schloss, Patrick D.;Westcott, Sarah L.;Weber, Carolyn F.
通讯作者：
Weber, Carolyn F.